Test

Tests in Ascend are validation mechanisms applied to data components like Read Components, Transforms, and more. These tests ensure that data meets specific quality and integrity criteria. They can range from simple checks, such as verifying non-null values in a column, to complex validations, like ensuring data consistency across different datasets.

Key Features of Tests

1. Versatility: Ascend supports a wide range of tests, enabling validation at both the column and component levels. This flexibility allows you to enforce data quality and integrity checks tailored to your specific needs. You can also build reusable tests that apply across multiple components.
2. Customization: You can create custom tests in SQL and Python, allowing you to define unique validation logic beyond the built-in options.
3. Inline Validation: You can define tests inline with data processing components, enabling immediate, context-aware validation.
4. Automation: Tests are automatically applied during data processing, providing continuous data quality assurance without manual intervention.

How Tests Work

You define tests within the configuration of flow components. When data flows through these components, the tests validate it against the specified criteria. If a test fails, the platform can halt processing and alert you, ensuring that data quality issues are promptly addressed. You can also isolate the problematic data for further analysis and resolution.

Types of Tests in Ascend

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See the Test Reference for more information on the syntax and capabilities of all test types.

• Column-Level Tests: Target specific columns within a dataset. These are useful for validating data types, uniqueness, range constraints, and more.
• Component-Level Tests: Apply to the entire output of a component. They ensure overall data structure, consistency, and accuracy.
• Standalone Tests: Defined independently of specific components, these tests are reusable and can validate data across multiple tables or components. They are particularly useful for cross-dataset validations or when applying a common set of standards across different parts of the pipeline.

Built-in and Custom Tests

• Test Library: Within component definitions, you can reference a suite of predefined tests covering common validation scenarios, such as checking for null values, ensuring column uniqueness, and verifying value ranges.
• Custom Tests: Enable you to define specific validation logic not covered by built-in tests. You can create these using SQL or Python, depending on the complexity and requirements of the validation.

Best Practices for Implementing Tests

• Comprehensive Testing: Implement a range of tests to cover all critical aspects of data quality and integrity. Use both built-in tests for common validations and custom tests for specific needs.
• Regular Review and Adjustment: Continuously review and adjust tests as data schemas and business requirements evolve to maintain their relevance and effectiveness.
• Performance Considerations: While thorough testing is essential, consider the performance impact. Optimize custom tests to minimize their effect on data processing times.

Conclusion

Tests are an integral part of Ascend's data engineering platform, ensuring that data pipelines produce reliable, high-quality data. By leveraging both built-in and custom tests, you can enforce strict data quality standards, automate validation processes, and significantly reduce the risk of data errors. Understanding how to effectively implement and use tests is crucial for maintaining data integrity and accuracy within Ascend's ecosystem.